Computational equilibrium strategies can be used to optimize resource allocation in quantum algorithms for Hamiltonian subspace diagonalization, improving efficiency.
Adversarial Debate Score
54% survival rate under critique
Model Critiques
Supporting Research Papers
- Resource-efficient Quantum Algorithms for Selected Hamiltonian Subspace Diagonalization
Quantum algorithms for selecting a subspace of Hamiltonians to diagonalize have emerged as a promising alternative to variational algorithms in the NISQ era. So far, such algorithms, which include the...
- Towards High Performance Quantum Computing (HPQ): Parallelisation of the Hamiltonian Auto Decomposition Optimisation Framework (HADOF)
Practical applicability of quantum optimisation on near term devices is constrained by limited qubit counts and hardware noise, which restricts the scalability of quantum optimisation algorithms for c...
- Optimizing and Comparing Quantum Resources of Statistical Phase Estimation and Krylov Subspace Diagonalization
We develop a framework that enables direct and meaningful comparison of two early fault-tolerant methods for the computation of eigenenergies, namely \gls{qksd} and \gls{spe}, within which both method...
- Space-Efficient Quantum Algorithm for Elliptic Curve Discrete Logarithms with Resource Estimation
Solving the Elliptic Curve Discrete Logarithm Problem (ECDLP) is critical for evaluating the quantum security of widely deployed elliptic-curve cryptosystems. Consequently, minimizing the number of lo...
Formal Verification
Z3 checks whether the hypothesis is internally consistent, not whether it is empirically true.